CN114870684A - Intelligent stirring equipment based on internet of things control - Google Patents

Intelligent stirring equipment based on internet of things control Download PDF

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Publication number
CN114870684A
CN114870684A CN202210531976.6A CN202210531976A CN114870684A CN 114870684 A CN114870684 A CN 114870684A CN 202210531976 A CN202210531976 A CN 202210531976A CN 114870684 A CN114870684 A CN 114870684A
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stirring
equipment
internet
time
intelligent
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CN114870684B (en
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万金荣
张前德
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Jiangsu Huijie Intelligent Mixing Technology Co ltd
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Jiangsu Huijie Intelligent Mixing Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F27/00—Mixers with rotary stirring devices in fixed receptacles; Kneaders
    • B01F27/80—Mixers with rotary stirring devices in fixed receptacles; Kneaders with stirrers rotating about a substantially vertical axis
    • B01F27/90—Mixers with rotary stirring devices in fixed receptacles; Kneaders with stirrers rotating about a substantially vertical axis with paddles or arms 
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F33/00—Other mixers; Mixing plants; Combinations of mixers
    • B01F33/80—Mixing plants; Combinations of mixers
    • B01F33/81—Combinations of similar mixers, e.g. with rotary stirring devices in two or more receptacles
    • B01F33/813—Combinations of similar mixers, e.g. with rotary stirring devices in two or more receptacles mixing simultaneously in two or more mixing receptacles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
    • B01F—MIXING, e.g. DISSOLVING, EMULSIFYING OR DISPERSING
    • B01F35/00—Accessories for mixers; Auxiliary operations or auxiliary devices; Parts or details of general application
    • B01F35/20—Measuring; Control or regulation
    • B01F35/22—Control or regulation
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/05—Programmable logic controllers, e.g. simulating logic interconnections of signals according to ladder diagrams or function charts
    • G05B19/054—Input/output
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

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  • Chemical & Material Sciences (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The invention relates to the field of stirring equipment, in particular to intelligent stirring equipment based on Internet of things control and a troubleshooting method.

Description

Intelligent stirring equipment based on internet of things control
Technical Field
The invention relates to the field of stirring equipment, in particular to intelligent stirring equipment based on Internet of things control and a troubleshooting method.
Background
Compared with food stirring equipment, the industrial stirring equipment has the characteristics of larger size, larger occupied area and higher concentration, and the control system made by most of the manufacturers of the stirring equipment is also used for electrically controlling low-voltage components in the traditional sense. Along with the rapid development of the internet, a plurality of clients need to monitor the running state of the stirring equipment at the mobile phone end and can control the stirring equipment at the mobile phone end. Thus, the conventional electrical control cannot meet the requirements of customers.
On the other hand, in the aspect of troubleshooting of the existing industrial stirring equipment, sensors including a rotation speed sensor, a torque sensor, a rotation speed sensor and the like are generally arranged on a motor and a circuit, whether the equipment has a fault or not is judged by directly acquiring the numerical value of the sensor, and then manual troubleshooting is arranged.
CN102259390A discloses a mortar stirring station flows based on thing networking specifically discloses install GPS module and wireless module additional, but real-time address information with the stirring station transmits for remote server to through weighing sensor, the total mass of stirring at every turn of acquisition that can be convenient, and transmit for remote server through wireless module, thereby make things convenient for the administrator to monitor in real time the stirring station position and stirring volume.
CN203471956U discloses a dry-mixed mortar storage mixer monitoring and management system based on internet of things, specifically discloses a system for monitoring production and use conditions of dry-mixed mortar by a storage mixer in real time, and has a remote display function and can be used by multiple customers to share data.
However, the above prior art still has the following technical problems:
1. the existing stirring equipment is only additionally provided with a remote control module, and can only work based on a preset control command or a control command actively sent by a user, so that the user cannot be actively prompted with a fault;
2. the existing stirring equipment relies on the direct data of the sensor for troubleshooting, and the fault can be reflected only when the direct data of the sensor is abnormal;
3. the existing stirring equipment has insufficient sensing capability for 'non-fault abnormity', and the equipment cannot exert all efficiency even if hidden non-fault abnormity is discovered, so that the production efficiency is reduced; the non-fault abnormity refers to the problem that no obvious abnormal signal occurs in the equipment, but the problems of poor debugging, insufficient lubrication and the like cause the problems of reduced efficiency, increased fluctuation, weakened stability and the like.
Disclosure of Invention
In order to solve the technical problems, the invention provides the following technical scheme:
the utility model provides an intelligence agitated vessel based on thing allies oneself with control, includes agitating unit and control system, control module includes touch-sensitive screen, PLC module and remote communication module, the remote communication module includes the network card socket, the network card socket is used for connecting the 4G network card, the 4G network card be used for with user's cell-phone remote communication, the user logs on the backstage of remote communication module on the cell-phone, works out the position and the relevant parameter of the state of the equipment that will show on the touch-sensitive screen on the backstage of remote communication module, and after the completion of working out, just can control equipment and look over the relevant running state of equipment at the cell-phone end.
Further, the control system is in remote communication with the internet of things server, a timer and a torque sensor are arranged on a motor spindle of the stirring device, and the timer is used for determining the stirring time T0 for the stirring device to complete one-time stirring;
the stirring time T0 is as follows: starting timing when the stirring device finishes material loading and starts, continuously recording the damping torque borne by the stirring main shaft by the torque sensor, when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1 and does not exceed an upper threshold value M2 and a lower threshold value M1 after the preset duration, considering that stirring is finished, and recording the time length from the stirring starting time to the stirring finishing time T2 as stirring time T0.
Furthermore, the intelligent stirring devices are in remote communication with the Internet of things server, the control system sends the device numbers and the stirring time T of each work to the Internet of things server, the Internet of things server performs Gaussian distribution inspection based on the stirring time T of any device to determine whether the stirring time T of the device meets Gaussian distribution, if not, the intelligent stirring device gives an alarm to a mobile phone of a user, feeds the alarm back to the intelligent stirring devices, and displays the alarm on a touch screen; if yes, marking the equipment as 'normal', calculating the mathematical expectation mu and the standard deviation sigma of the equipment marked as 'normal', carrying out cluster analysis on the mathematical expectation mu and the standard deviation sigma of each equipment, marking the equipment with discrete mathematical expectation mu and/or standard deviation sigma as 'early warning', and sending the equipment number to the mobile phone of the user.
Further, the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
Figure BDA0003645480470000041
where x (i) is the ith order statistic, i.e., the ith smallest number in the sample;
Figure BDA0003645480470000042
is the average of the samples;
constant a i Calculated by the following formula:
Figure BDA0003645480470000043
wherein m ═ m (m) 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of the ordered independent identically distributed statistic, m, sampled from a standard Gaussian distributed random variable 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the above ordered statistics.
Further, the internet of things server is pre-stored with an SPSS program, the equipment meeting gaussian distribution is marked as "normal", and the SPPS program is used to perform cluster analysis on the mathematical expectation μ and the standard deviation σ of each equipment marked as "normal".
A method for troubleshooting an intelligent stirring device based on Internet of things control is carried out according to the intelligent stirring device and comprises the following steps:
s1, preparation step: an engineer uses professional programming software to program a logic control program of the stirring equipment in a computer and downloads the logic control program into a PLC module; then, a professional touch screen control picture and related parameters in a computer are compiled and downloaded to the touch screen. Then, connecting the touch screen with the PLC by using a network cable; carrying out communication inspection on each intelligent stirring device, and completing remote communication connection between the intelligent stirring device and a mobile phone of a user and a server of the Internet of things;
s2, pre-experiment step: loading a preset amount of materials, starting stirring, recording a stirring time and torque relation curve, stopping the equipment after the engineer determines that the stirring is finished based on a stirring standard, continuously stirring for a certain time, deriving a stirring time and torque relation curve, and determining an upper threshold value M2 and a lower threshold value M1 of damping torque and required duration time based on the curve;
s3, working step: working according to a set stirring program;
s4, data proofreading step: the method specifically comprises the following steps:
s41: gaussian analysis: the control system sends the equipment number and the stirring time T of each work to the Internet of things server, the Internet of things server performs Gaussian distribution inspection based on the stirring time T of any equipment to determine whether the stirring time T of the equipment meets Gaussian distribution or not, if not, the equipment is marked as abnormal, the mobile phone of a user is alarmed, the alarm is fed back to the intelligent stirring equipment, and the alarm is displayed on the touch screen; if yes, calculating the mathematical expectation mu and the standard deviation sigma of the equipment;
s42: clustering analysis: and for the equipment with the stirring time meeting the Gaussian distribution, marking the equipment as normal, carrying out cluster analysis on the mathematical expectation mu and the standard deviation sigma of the equipment marked as normal, marking the equipment with the discrete mathematical expectation mu and/or the standard deviation sigma as early warning, and sending the equipment number to the mobile phone of the user.
Further, the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
Figure BDA0003645480470000061
where x (i) is the ith order statistic, i.e., the ith smallest number in the sample;
Figure BDA0003645480470000062
is the average of the samples;
constant a i Calculated by the following formula:
Figure BDA0003645480470000063
wherein m ═ m (m) 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of the ordered independent identically distributed statistic, m, sampled from a standard Gaussian distributed random variable 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the above ordered statistics.
Further, the internet of things server is pre-stored with an SPSS program, the equipment meeting gaussian distribution is marked as "normal", and the SPPS program is used to perform cluster analysis on the mathematical expectation μ and the standard deviation σ of each equipment marked as "normal".
Further, the clustering analysis is a k-means clustering algorithm.
Further, the clustering analysis is a hierarchical clustering algorithm.
Further, the preliminary experiment step of the invention also comprises drawing of a weight-stirring time curve, and the specific method is that the mass of the loaded materials is determined at the bottom of the stirring equipment according to the weight sensor, then the stirring equipment is started, the torque sensor continuously records the damping torque applied to the stirring main shaft, when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1 and does not exceed an upper threshold value M2 and a lower threshold value M1 after the preset duration time, the stirring is considered to be completed, the time duration from the stirring starting time to the stirring completing time T2 is recorded as the stirring time T0, and in the subsequent S4 and data proofreading step, the material weight and the stirring time are used as parameters to determine whether the two-dimensional Gaussian distribution is met.
(III) advantageous effects
Compared with the prior art, the invention has the following beneficial effects:
1. the stirring equipment provided by the invention is transformed through Internet of things, can work based on remote control of a user and can also work based on remote programming, so that the networking capability of the equipment is improved, and the remote monitoring and operating capability of the user on the equipment is improved.
2. The invention carries out the 'equipment abnormity' examination based on the statistical rule, in particular to the stirring equipment which completes the debugging, the working time of the preset stirring task should meet the Gaussian distribution.
3. The invention carries out the investigation of 'non-fault abnormity' based on the statistical law, particularly, when the stirring equipment works daily, the problems of motor coil aging, bearing lack of lubricating oil and the like can occur, the direct fault of the equipment can not be caused, but the efficiency of the equipment can be reduced, namely, 'non-fault abnormity' occurs, the application carries out the cluster analysis on a plurality of equipment which accord with the Gaussian distribution by a statistical method, the equipment with the discrete condition can be found, particularly, the stirring time can be increased due to the leakage flux of the motor, the mathematical expectation mu of the Gaussian distribution can be increased, the detection precision can be reduced due to the pollution of a torque sensor, the equipment with unstable material resistance can be judged in advance to complete the stirring, the mathematical expectation mu of the Gaussian distribution can be reduced, or the stirring force can be uneven due to the lack of lubricating oil of a shaft system, the standard deviation σ will increase; the equipment and the method can timely discharge the non-fault abnormity, avoid equipment condition deterioration and ensure production efficiency.
Drawings
FIG. 1 is a schematic structural view of a stirring apparatus of the present invention;
FIG. 2 is a schematic diagram of the structure of the Internet of things based on a plurality of stirring devices;
FIG. 3 is a schematic view of the mixing apparatus of the present invention controlling Yuanjiang;
FIG. 4 is a torque-stir time plot of the present invention.
In the figure: 1. a node; 11. an electronic map module; 12. a gridding module; 13. a task broadcasting module; 14. a consensus module; 141. a publishing submodule; 142. a resource calling submodule; 143. an evaluation submodule; 144. a decision sub-module; 145. a voting sub-module; 15. and executing the module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
referring to the drawings, in which like numerals represent like parts,
the utility model provides an intelligence agitated vessel based on thing allies oneself with control, includes agitating unit 1 and control system 2, control module includes touch-sensitive screen 21, PLC module 22 and remote communication module 23, its characterized in that: the remote communication module 23 comprises a network card socket, the network card socket is used for connecting a 4G network card, the 4G network card is used for remote communication with a mobile phone of a user, the user logs in a background of the remote communication module on the mobile phone, the point position of the state of the equipment to be displayed on the touch screen and related parameters are compiled on the background of the remote communication module, and after the compiling is completed, the equipment can be controlled and the related running state of the equipment can be checked at the mobile phone end. The control system 2 is in remote communication with the internet of things server 3, a timer 4 and a torque sensor 5 are arranged on a motor spindle of the stirring device 1, and the timer 4 is used for determining the stirring time T0 for the stirring device 1 to complete one-time stirring;
the stirring time T0 is as follows: starting timing when the stirring device 1 finishes material loading and starts, continuously recording damping torque borne by a stirring main shaft by the torque sensor 5, when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1, recording the time T1, continuously timing, and when the damping torque exceeds between a preset upper threshold value M2 and a preset lower threshold value M1, judging whether the duration is greater than a preset duration threshold value, if not, considering that the timing is invalid, repeatedly detecting, if so, considering that stirring is finished, and recording the time duration from the stirring starting time to the stirring finishing time T2 as stirring time T0.
The intelligent stirring devices are in remote communication with the Internet of things server 3, the control system 2 sends the device numbers and the stirring time T of each work to the Internet of things server 3, the Internet of things server 3 performs Gaussian distribution inspection based on the stirring time T of any device to determine whether the stirring time T of the device meets Gaussian distribution, if not, the device gives an alarm to a mobile phone of a user, feeds the alarm back to the intelligent stirring devices, and displays the alarm on the touch screen 21; if yes, marking the equipment as 'normal', calculating the mathematical expectation mu and the standard deviation sigma of the equipment marked as 'normal', carrying out cluster analysis on the mathematical expectation mu and the standard deviation sigma of each equipment, marking the equipment with discrete mathematical expectation mu and/or standard deviation sigma as 'early warning', and sending the equipment number to the mobile phone of the user.
The Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
Figure BDA0003645480470000111
where x (i) is the ith order statistic, i.e., the ith minimum number in the sample;
Figure BDA0003645480470000112
is the average of the samples;
constant a i Calculated by the following formula:
Figure BDA0003645480470000113
wherein m ═ m (m) 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of the ordered independent identically distributed statistic, m, sampled from a standard Gaussian distributed random variable 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the above ordered statistics.
The internet of things server 3 is pre-stored with an SPSS program, the equipment satisfying gaussian distribution is marked as "normal", and the mathematical expectation μ and the standard deviation σ of each equipment marked as "normal" are subjected to cluster analysis by using the SPPS program.
Example two:
the method for troubleshooting the intelligent stirring equipment based on the internet of things control is characterized by being carried out based on the intelligent stirring equipment and comprising the following steps of:
s1, preparation step: an engineer uses professional programming software to program a logic control program of the stirring equipment in a computer and then downloads the logic control program into a PLC module; then, a professional touch screen control picture and related parameters in a computer are compiled and downloaded to the touch screen. Then, connecting the touch screen with the PLC by using a network cable; carrying out communication inspection on each intelligent stirring device, and completing remote communication connection between the intelligent stirring device and a mobile phone of a user and the Internet of things server 3;
s2, pre-experiment step: loading a preset amount of materials, starting stirring, recording a stirring time and torque relation curve, stopping the equipment after the engineer determines that the stirring is finished based on a stirring standard, continuously stirring for a certain time, deriving a stirring time and torque relation curve, and determining an upper threshold value M2 and a lower threshold value M1 of damping torque and required duration time based on the curve;
s3, working step: working according to a set stirring program;
s4, data proofreading step: the method specifically comprises the following steps:
s41: gaussian analysis: the control system 2 sends the equipment number and the stirring time T of each work to the Internet of things server 3, the Internet of things server 3 performs Gaussian distribution inspection based on the stirring time T of any equipment to determine whether the stirring time T of the equipment meets Gaussian distribution or not, if not, the equipment is marked as abnormal, the mobile phone of a user is alarmed, the alarm is fed back to the intelligent stirring equipment, and the alarm is displayed on the touch screen 21; if yes, calculating the mathematical expectation mu and the standard deviation sigma of the equipment;
s42: clustering analysis: and for the equipment with the stirring time meeting the Gaussian distribution, marking the equipment as normal, carrying out cluster analysis on the mathematical expectation mu and the standard deviation sigma of the equipment marked as normal, marking the equipment with the discrete mathematical expectation mu and/or the standard deviation sigma as early warning, and sending the equipment number to the mobile phone of the user.
The Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
Figure BDA0003645480470000131
where x (i) is the ith order statistic, i.e., the ith smallest number in the sample;
Figure BDA0003645480470000132
is the average of the samples;
constant a i Calculated by the following formula:
Figure BDA0003645480470000133
wherein m ═ m (m) 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of the ordered independent identically distributed statistic, m, sampled from a standard Gaussian distributed random variable 1 ，...，m n Obtained by preliminary experimentsObtaining; v is the covariance of the above ordered statistics.
The internet-of-things server 3 is pre-stored with an SPSS program, marks the devices satisfying gaussian distribution as "normal", and performs cluster analysis on the mathematical expectation μ and the standard deviation σ of each device marked as "normal" by using an SPPS program. The clustering analysis is a k-means clustering algorithm or a hierarchical clustering algorithm.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (10)

1. The utility model provides an intelligence agitated vessel based on thing allies oneself with control, includes agitating unit (1) and control system (2), control module includes touch-sensitive screen (21), PLC module (22) and remote communication module (23), its characterized in that: the remote communication module (23) comprises a network card socket, the network card socket is used for connecting a 4G network card, the 4G network card is used for remote communication with a mobile phone of a user, the user logs in a background of the remote communication module on the mobile phone, the point position of the state of the equipment to be displayed on the touch screen and related parameters are compiled on the background of the remote communication module, and after the compiling is completed, the equipment can be controlled and the related running state of the equipment can be checked at the mobile phone end.
2. The intelligent stirring device based on the internet of things control as claimed in claim 1, wherein: the control system (2) is in remote communication with the Internet of things server (3), a timer (4) and a torque sensor (5) are arranged on a motor spindle of the stirring device (1), and the timer (4) is used for determining the stirring time T0 for the stirring device (1) to complete one-time stirring;
the stirring time T0 is as follows: starting timing when the stirring device (1) finishes material loading and starts, continuously recording damping torque borne by a stirring main shaft by a torque sensor (5), when the damping torque falls between a preset upper threshold value M2 and a preset lower threshold value M1 and does not exceed an upper threshold value M2 and a lower threshold value M1 after a preset duration, considering that stirring is finished, and recording the time duration from the stirring starting time to the stirring finishing time T2 as stirring time T0.
3. The intelligent stirring device based on the internet of things control as claimed in claim 1 or 2, wherein: the intelligent stirring devices are in remote communication with the Internet of things server (3), the control system (2) sends the device numbers and the stirring time T of each work to the Internet of things server (3), the Internet of things server (3) performs Gaussian distribution inspection based on the stirring time T of any device to determine whether the stirring time T of the device meets Gaussian distribution, if not, the device gives an alarm to a mobile phone of a user, feeds the alarm back to the intelligent stirring devices, and displays the alarm on a touch screen (21); if yes, marking the equipment as 'normal', calculating the mathematical expectation mu and the standard deviation sigma of the equipment marked as 'normal', carrying out cluster analysis on the mathematical expectation mu and the standard deviation sigma of each equipment, marking the equipment with discrete mathematical expectation mu and/or standard deviation sigma as 'early warning', and sending the equipment number to the mobile phone of the user.
4. The intelligent stirring device based on the internet of things control as claimed in any one of claims 1 to 3, wherein: the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
Figure FDA0003645480460000021
where x (i) is the ith order statistic, i.e., the ith smallest number in the sample;
Figure FDA0003645480460000022
is the average of the samples;
constant a i Calculated by the following formula:
Figure FDA0003645480460000023
wherein m ═ m (m) 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of the ordered independent identically distributed statistic, m, sampled from a standard Gaussian distributed random variable 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the above ordered statistics.
5. The intelligent stirring device based on the internet of things control as claimed in any one of claims 1 to 4, wherein: the Internet of things server (3) is pre-stored with an SPSS program, equipment meeting Gaussian distribution is marked as 'normal', and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each equipment marked as 'normal'.
6. A method for troubleshooting an intelligent stirring device based on an internet of things control, wherein the troubleshooting method is carried out based on the intelligent stirring device according to any one of claims 1-5 and comprises the following steps:
s1, preparation step: an engineer uses professional programming software to program a logic control program of the stirring equipment in a computer and then downloads the logic control program into a PLC module; then, a professional touch screen control picture and related parameters in a computer are compiled and downloaded to the touch screen. Then, connecting the touch screen with the PLC by using a network cable; carrying out communication inspection on each intelligent stirring device, and completing remote communication connection between the intelligent stirring device and a user mobile phone and an Internet of things server (3);
s2, pre-experiment step: loading a preset amount of materials, starting stirring, recording a stirring time and torque relation curve, stopping the equipment after the engineer determines that the stirring is finished based on a stirring standard, continuously stirring for a certain time, deriving a stirring time and torque relation curve, and determining an upper threshold value M2 and a lower threshold value M1 of damping torque and required duration time based on the curve;
s3, working step: working according to a set stirring program;
s4, data proofreading step: the method specifically comprises the following steps:
s41: gaussian analysis: the control system (2) sends the equipment number and the stirring time T of each work to the Internet of things server (3), the Internet of things server (3) conducts Gaussian distribution inspection based on the stirring time T of any equipment to determine whether the stirring time T of the equipment meets Gaussian distribution or not, if not, the equipment is marked as abnormal, a mobile phone of a user is alarmed, the alarm is fed back to the intelligent stirring equipment, and the alarm is displayed on the touch screen (21); if yes, calculating the mathematical expectation mu and the standard deviation sigma of the equipment;
s42: clustering analysis: and for the equipment with the stirring time meeting the Gaussian distribution, marking the equipment as normal, carrying out cluster analysis on the mathematical expectation mu and the standard deviation sigma of the equipment marked as normal, marking the equipment with the discrete mathematical expectation mu and/or the standard deviation sigma as early warning, and sending the equipment number to the mobile phone of the user.
7. The method for troubleshooting the intelligent stirring device based on the IOT control as claimed in claim 6, characterized in that:
the Gaussian distribution test is an S-W test; the statistics of the S-W test are as follows:
Figure FDA0003645480460000041
where x (i) is the ith order statistic, i.e., the ith smallest number in the sample;
Figure FDA0003645480460000051
is the average of the samples;
constant a i Calculated by the following formula:
Figure FDA0003645480460000052
wherein m ═ m (m) 1 ，...，m n ) T Wherein m is 1 ，...，m n Is the expected value of the ordered independent identically distributed statistic, m, sampled from a standard Gaussian distributed random variable 1 ，...，m n Obtained by preliminary experiments; v is the covariance of the above ordered statistics.
8. The method for troubleshooting the intelligent stirring device based on the IOT control as claimed in claim 7, characterized in that:
the Internet of things server (3) is pre-stored with an SPSS program, equipment meeting Gaussian distribution is marked as 'normal', and the SPPS program is utilized to perform cluster analysis on mathematical expectation mu and standard deviation sigma of each equipment marked as 'normal'.
9. The method for troubleshooting the intelligent stirring device based on the IOT control as claimed in claim 8, characterized in that: the clustering analysis is a k-means clustering algorithm.
10. The method for troubleshooting the intelligent stirring device based on the IOT control as claimed in claim 8, characterized in that: the clustering analysis is a hierarchical clustering algorithm.
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